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Radio Frequency Interference Sensing and Mitigation in Wireless Receivers

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## Radio Frequency Interference Sensing and Mitigation in Wireless Receivers

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**Wireless Networking and Communications Group**Brian L. Evans Lead Graduate Students Aditya Chopra, KapilGulati, and Marcel Nassar In collaboration with Eddie Xintian Lin, Alberto AlcocerOchoa, Srikathyayani Srikanteswara, and Keith R. Tinsley at Intel Labs Radio Frequency Interference Sensing and Mitigation in Wireless Receivers Talk at Intel Labs at Hillsboro, Oregon**Outline**• Introduction • Problem Definition • Statistical Modeling of Radio Frequency Interference • Receiver Design to Mitigate Radio Frequency Interference • Conclusions • Future work RFI Wireless Networking and Communications Group**Introduction**(WiMAX Basestation) (Microwave) (Wi-Fi) (Wi-Fi) (WiMAX) antenna (WiMAX Mobile) • Wireless Communication Sources • Closely located sources • Coexisting protocols Non-Communication Sources Electromagnetic radiations baseband processor (Bluetooth) • Computational Platform • Clocks, busses, processors • Co-located transceivers Wireless Networking and Communications Group**Radio Frequency Interference (RFI)**• Limits wireless communication performance • Impact of LCD noise on throughput for an embedded Wi-Fi (IEEE 802.11g) receiver[Shi, Bettner, Chinn, Slattery & Dong, 2006] Wireless Networking and Communications Group**Problem Definition**• Problem: Co-channel and adjacent channel interference, and platform noise degrade communication performance • Approach: Statistical modeling of RFI • Solution: Receiver design • Listen to the environment • Estimate parameters for RFI statistical models • Use parameters to mitigate RFI • Goal: Improve communication performance • 10-100x reduction in bit error rate (this talk) • 10-100x improvement in network throughput (future work) Wireless Networking and Communications Group**Statistical Modeling of RFI**• Multiple communication and non-communication sources • System Model • Point process to model interferer locations • Poisson(uncoordinated, e.g. ad hoc) • Poisson-Poisson cluster(with user clustering, e.g. femtocell) • Sum interference • Goal: Closed form statistics to model tail probability Interferer emissions Pathloss Fading Tail probability governs communication performance Wireless Networking and Communications Group**Statistical Models (isotropic, zero centered)**7 • Symmetric Alpha Stable [Furutsu & Ishida, 1961] [Sousa, 1992] • Characteristic function • Gaussian Mixture Model [Sorenson & Alspach, 1971] • Amplitude distribution • Middleton Class A (w/o Gaussian component) [Middleton, 1977] Wireless Networking and Communications Group**Poisson Field of Interferers**Middleton Class A (form of Gaussian Mixture) Symmetric Alpha Stable • Dense Wi-Fi networks • Networks with contention based medium access • Cellular networks • Hotspots (e.g. café) • Sensor networks • Ad hoc networks Wireless Networking and Communications Group**Poisson-Poisson Cluster Field of Interferers**Gaussian Mixture Model Symmetric Alpha Stable • In-cell and out-of-cell femtocell users in femtocell networks • Out-of-cell femtocell users in femtocell networks • Cluster of hotspots (e.g. marketplace) Wireless Networking and Communications Group**Fitting Measured Laptop RFI Data**10 • Statistical-physical models fit data better than Gaussian • Radiated platform RFI • 25 RFI data sets from Intel • 50,000 samples at 100 MSPS • Laptop activity unknown to us • Smaller KL divergence • Closer match in distribution • Does not imply close match in tail probabilities • Platform RFI sources • May not be Poisson distributed • May not have identical emissions Wireless Networking and Communications Group**Results on Measured RFI Data**11 • For measurement set #23 • Tail probability governs communication performance • Bit error rate • Outage probability Wireless Networking and Communications Group**Receiver Design to Mitigate RFI**• Design receivers using knowledge of RFI statistics Guard zone RTS CTS Example: Wi-Fi networks RTS / CTS: Request / Clear to send Interference statistics similar to Case III Wireless Networking and Communications Group**RFI Mitigation in SISO systems**Interference + Thermal noise • Communication performance Pulse Shaping Pre-filtering Matched Filter Detection Rule Binary Phase Shift Keying Wireless Networking and Communications Group**RFI Mitigation in 2 x 2 MIMO systems**Improvement in communication performance over conventional Gaussian ML receiver at symbol error rate of 10-2 Conventional Gaussian ML Receiver Proposed Receivers Communication Performance (A = 0.1, 1= 0.01, 2= 0.1, k = 0.4) Wireless Networking and Communications Group**RFI Mitigation in 2 x 2 MIMO systems**15 Complexity Analysis for decoding M-level QAM modulated signal Conventional Gaussian ML Receiver Complexity Analysis Proposed Receivers Communication Performance (A = 0.1, 1= 0.01, 2= 0.1, k = 0.4) Wireless Networking and Communications Group**RFI Mitigation Using Error Correction**• Turbo decoder • Decoding depends on the RFI statistics • 10 dB improvement at BER 10-5 can be achieved using accurate RFI statistics [Umehara, 2003] - Decoder 1 Interleaver Parity 1 - Systematic Data Interleaver - Decoder 2 Interleaver Parity 2 - Wireless Networking and Communications Group**Summary**• Radio frequency interference affects wireless transceivers • RFI mitigation can improve communication performance • Our contributions Wireless Networking and Communications Group**Current and Future Work**• RFI Modeling • Temporal modeling • Multi-antenna modeling • Analysis and Bounds on Communication Performance • Physical layer (filtering, detection, and error correction) • Medium access control layer protocols • RFI Mitigation • Extensions to multicarrier (OFDM) systems • Extensions to multi-antenna (MIMO) systems • Extensions to multipath channels Wireless Networking and Communications Group**Related Publications**• Journal Publications • K. Gulati, B. L. Evans, J. G. Andrews, and K. R. Tinsley, “Statistics of Co-Channel Interference in a Field of Poisson and Poisson-Poisson Clustered Interferers”, IEEE Transactions on Signal Processing, submitted Nov. 29, 2009. • M. Nassar, K. Gulati, M. R. DeYoung, B. L. Evans and K. R. Tinsley, “Mitigating Near-Field Interference in Laptop Embedded Wireless Transceivers”, Journal of Signal Processing Systems, Mar. 2009, invited paper. • Conference Publications • M. Nassar, X. E. Lin, and B. L. Evans, “Stochastic Modeling of Microwave Oven Interference in WLANs”, Int. Global Comm. Conf., Dec. 6-10, 2010, submitted. • K. Gulati, B. L. Evans, and K. R. Tinsley, “Statistical Modeling of Co-Channel Interference in a Field of Poisson Distributed Interferers”, Proc.IEEE Int. Conf. on Acoustics, Speech, and Signal Proc., Mar. 14-19, 2010. • K. Gulati, A. Chopra, B. L. Evans, and K. R. Tinsley, “Statistical Modeling of Co-Channel Interference”, Proc.IEEE Int. Global Communications Conf., Nov. 30-Dec. 4, 2009. • Cont… Wireless Networking and Communications Group**Related Publications**• Conference Publications (cont…) • A. Chopra, K. Gulati, B. L. Evans, K. R. Tinsley, and C. Sreerama, “Performance Bounds of MIMO Receivers in the Presence of Radio Frequency Interference”, Proc.IEEE Int. Conf. on Acoustics, Speech, and Signal Proc., Apr. 19-24, 2009. • K. Gulati, A. Chopra, R. W. Heath, Jr., B. L. Evans, K. R. Tinsley, and X. E. Lin, “MIMO Receiver Design in the Presence of Radio Frequency Interference”, Proc.IEEE Int. Global Communications Conf., Nov. 30-Dec. 4th, 2008. • M. Nassar, K. Gulati, A. K. Sujeeth, N. Aghasadeghi, B. L. Evans and K. R. Tinsley, “Mitigating Near-Field Interference in Laptop Embedded Wireless Transceivers”, Proc.IEEE Int. Conf. on Acoustics, Speech, and Signal Proc., Mar. 30-Apr. 4, 2008. • Software Releases • K. Gulati, M. Nassar, A. Chopra, B. Okafor, M. R. DeYoung, N. Aghasadeghi, A. Sujeeth, and B. L. Evans, "Radio Frequency Interference Modeling and Mitigation Toolbox in MATLAB", version 1.4.1 beta, Apr. 11, 2010. Wireless Networking and Communications Group**UT Austin RFI Modeling & Mitigation Toolbox**• Freely distributable toolbox in MATLAB • Simulation environment for RFI modeling and mitigation • RFI generation • Measured RFI fitting • Parameter estimation algorithms • Filtering and detection methods • Demos for RFI modeling and mitigation • Latest Toolbox Release Version 1.4.1 beta, Apr. 11, 2010 Snapshot of a demo http://users.ece.utexas.edu/~bevans/projects/rfi/software/index.html Wireless Networking and Communications Group**Usage Scenario #1**RFI Toolbox User System Simulator(e.g. WiMAX simulator) Wireless Networking and Communications Group**Usage Scenario #2**Measured RFI data Statistical Modeling DEMO RFI Toolbox SISO Communication Performance DEMO MIMO Communication Performance DEMO File Transfer DEMO Wireless Networking and Communications Group**Thanks !**Wireless Networking and Communications Group**References**RFI Modeling • D. Middleton, “Non-Gaussian noise models in signal processing for telecommunications: New methods and results for Class A and Class B noise models”, IEEE Trans. Info. Theory, vol. 45, no. 4, pp. 1129-1149, May 1999. • K. Furutsu and T. Ishida, “On the theory of amplitude distributions of impulsive random noise,” J. Appl. Phys., vol. 32, no. 7, pp. 1206–1221, 1961. • J. Ilow and D . Hatzinakos, “Analytic alpha-stable noise modeling in a Poisson field of interferers or scatterers”, IEEE transactions on signal processing, vol. 46, no. 6, pp. 1601-1611, 1998. • E. S. Sousa, “Performance of a spread spectrum packet radio network link in a Poisson field of interferers,” IEEE Transactions on Information Theory, vol. 38, no. 6, pp. 1743–1754, Nov. 1992. • X. Yang and A. Petropulu, “Co-channel interference modeling and analysis in a Poisson field of interferers in wireless communications,” IEEE Transactions on Signal Processing, vol. 51, no. 1, pp. 64–76, Jan. 2003. • E. Salbaroli and A. Zanella, “Interference analysis in a Poisson field of nodes of finite area,” IEEE Transactions on Vehicular Technology, vol. 58, no. 4, pp. 1776–1783, May 2009. • M. Z. Win, P. C. Pinto, and L. A. Shepp, “A mathematical theory of network interference and its applications,” Proceedings of the IEEE, vol. 97, no. 2, pp. 205–230, Feb. 2009. Wireless Networking and Communications Group**References**Parameter Estimation • S. M. Zabin and H. V. Poor, “Efficient estimation of Class A noise parameters via the EM [Expectation-Maximization] algorithms”, IEEE Trans. Info. Theory, vol. 37, no. 1, pp. 60-72, Jan. 1991 . • G. A. Tsihrintzis and C. L. Nikias, "Fast estimation of the parameters of alpha-stable impulsive interference", IEEE Trans. Signal Proc., vol. 44, Issue 6, pp. 1492-1503, Jun. 1996. Communication Performance of Wireless Networks • R. Ganti and M. Haenggi, “Interference and outage in clustered wireless ad hoc networks,” IEEE Transactions on Information Theory, vol. 55, no. 9, pp. 4067–4086, Sep. 2009. • A. Hasan and J. G. Andrews, “The guard zone in wireless ad hoc networks,” IEEE Transactions on Wireless Communications, vol. 4, no. 3, pp. 897–906, Mar. 2007. • X. Yang and G. de Veciana, “Inducing multiscale spatial clustering using multistage MAC contention in spread spectrum ad hoc networks,” IEEE/ACM Transactions on Networking, vol. 15, no. 6, pp. 1387–1400, Dec. 2007. • S. Weber, X. Yang, J. G. Andrews, and G. de Veciana, “Transmission capacity of wireless ad hoc networks with outage constraints,” IEEE Transactions on Information Theory, vol. 51, no. 12, pp. 4091-4102, Dec. 2005. Wireless Networking and Communications Group**References**Communication Performance of Wireless Networks (cont…) • S. Weber, J. G. Andrews, and N. Jindal, “Inducing multiscale spatial clustering using multistage MAC contention in spread spectrum ad hoc networks,” IEEE Transactions on Information Theory, vol. 53, no. 11, pp. 4127-4149, Nov. 2007. • J. G. Andrews, S. Weber, M. Kountouris, and M. Haenggi, “Random access transport capacity,” IEEE Transactions On Wireless Communications, Jan. 2010, submitted. [Online]. Available: http://arxiv.org/abs/0909.5119 • M. Haenggi, “Local delay in static and highly mobile Poisson networks with ALOHA," in Proc. IEEE International Conference on Communications, Cape Town, South Africa, May 2010. • F. Baccelli and B. Blaszczyszyn, “A New Phase Transitions for Local Delays in MANETs,” in Proc. of IEEE INFOCOM, San Diego, CA,2010, to appear. Receiver Design to Mitigate RFI • A. Spaulding and D. Middleton, “Optimum Reception in an Impulsive Interference Environment-Part I: Coherent Detection”, IEEE Trans. Comm., vol. 25, no. 9, Sep. 1977 • J.G. Gonzalez and G.R. Arce, “Optimality of the Myriad Filter in Practical Impulsive-Noise Environments”, IEEE Trans. on Signal Processing, vol 49, no. 2, Feb 2001 Wireless Networking and Communications Group**References**Receiver Design to Mitigate RFI (cont…) • S. Ambike, J. Ilow, and D. Hatzinakos, “Detection for binary transmission in a mixture of Gaussian noise and impulsive noise modelled as an alpha-stable process,” IEEE Signal Processing Letters, vol. 1, pp. 55–57, Mar. 1994. • G. R. Arce, Nonlinear Signal Processing: A Statistical Approach, John Wiley & Sons, 2005. • Y. Eldar and A. Yeredor, “Finite-memory denoising in impulsive noise using Gaussian mixture models,” IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing, vol. 48, no. 11, pp. 1069-1077, Nov. 2001. • J. H. Kotecha and P. M. Djuric, “Gaussian sum particle ltering,” IEEE Transactions on Signal Processing, vol. 51, no. 10, pp. 2602-2612, Oct. 2003. • J. Haring and A.J. Han Vick, “Iterative Decoding of Codes Over Complex Numbers for Impulsive Noise Channels”, IEEE Trans. On Info. Theory, vol 49, no. 5, May 2003. • Ping Gao and C. Tepedelenlioglu. “Space-time coding over mimo channels with impulsive noise”, IEEE Trans. on Wireless Comm., 6(1):220–229, January 2007. RFI Measurements and Impact • J. Shi, A. Bettner, G. Chinn, K. Slattery and X. Dong, "A study of platform EMI from LCD panels – impact on wireless, root causes and mitigation methods,“ IEEE International Symposium onElectromagnetic Compatibility, vol.3, no., pp. 626-631, 14-18 Aug. 2006 Wireless Networking and Communications Group**Backup Slides**• Introduction • Interference avoidance , alignment, and cancellation methods • Femtocell networks • Statistical Modeling of RFI • Computational platform noise • Impact of RFI • Assumptions for RFI Modeling • Transients in digital FIR filters • Poisson field of interferers • Poisson-Poisson cluster field of interferers Backup Backup Backup Backup Backup Backup Backup Backup Wireless Networking and Communications Group**Backup Slides (cont…)**• Gaussian Mixture vs. Alpha Stable • Middleton Class A, B, and C models • Middleton Class A model • Expectation maximization overview • Results: EM for Middleton Class A • Symmetric Alpha Stable • Extreme order statistics based estimator for Alpha Stable • Video over impulsive channels • Demonstration #1 • Demonstration #2 Backup Backup Backup Backup Backup Backup Backup Backup Backup Wireless Networking and Communications Group**Backup Slides (cont…)**• RFI mitigation in SISO systems • Our contributions • Results: Class A Detection • Results: Alpha Stable Detection • RFI mitigation in MIMO systems • Our contributions • Performance bounds for SISO systems • Performance bounds for MIMO systems • Extensions for multicarrier systems • Turbo codes in impulsive channels Backup Backup Backup Backup Backup Backup Backup Backup Wireless Networking and Communications Group**Interference Mitigation Techniques**• Interference avoidance • CSMA / CA • Interference alignment • Example: [Cadambe & Jafar, 2007] Return Wireless Networking and Communications Group**Interference Mitigation Techniques (cont…)**• Interference cancellation Ref: J. G. Andrews, ”Interference Cancellation for Cellular Systems: A Contemporary Overview”, IEEE Wireless Communications Magazine, Vol. 12, No. 2, pp. 19-29, April 2005 Return Wireless Networking and Communications Group**Femtocell Networks**Reference: V. Chandrasekhar, J. G. Andrews and A. Gatherer, "Femtocell Networks: a Survey", IEEE Communications Magazine, Vol. 46, No. 9, pp. 59-67, September 2008 Return Wireless Networking and Communications Group**Common Spectral Occupancy**Return Wireless Networking and Communications Group**Impact of RFI**• Calculated in terms of desensitization (“desense”) • Interference raises noise floor • Receiver sensitivity will degrade to maintain SNR • Desensitization levels can exceed 10 dB for 802.11a/b/g due to computational platform noise [J. Shi et al., 2006] Case Sudy: 802.11b, Channel 2, desense of 11dB • More than 50% loss in range • Throughput loss up to ~3.5 Mbps for very low receive signal strengths (~ -80 dbm) Return Wireless Networking and Communications Group**Impact of LCD clock on 802.11g**• Pixel clock 65 MHz • LCD Interferers and 802.11g center frequencies Return Wireless Networking and Communications Group**Assumptions for RFI Modeling**• Key assumptions for Middleton and Alpha Stable models[Middleton, 1977][Furutsu & Ishida, 1961] • Infinitely many potential interfering sources with same effective radiation power • Power law propagation loss • Poisson field of interferers with uniform intensity l • Pr(number of interferers = M |area R) ~ Poisson(M; lR) • Uniformly distributed emission times • Temporally independent (at each sample time) • Limitations • Alpha Stable models do not include thermal noise • Temporal dependence may exist Return Wireless Networking and Communications Group**Transients in Digital FIR Filters**• 25-Tap FIR Filter • Low pass • Stopband freq. 0.22 (normalized) Input Output Return Freq = 0.16 Interference duration = 100 x 1/0.22 Interference duration = 10 * 1/0.22 Transients Transients Significant w.r.t. Steady State Transients Ignorable w.r.t. Steady State Wireless Networking and Communications Group**Poisson Field of Interferers**• Interferers distributed over parametric annular space • Log-characteristic function Return Wireless Networking and Communications Group**Poisson Field of Interferers**Return Wireless Networking and Communications Group**Poisson Field of Interferers**Return • Simulation Results (tail probability) Case III: Infinite-area with guard zone Case I: Entire Plane Gaussian and Middleton Class A models are not applicable since mean intensity is infinite Wireless Networking and Communications Group**Poisson Field of Interferers**• Simulation Results (tail probability) Return Case II: Finite area annular region Wireless Networking and Communications Group**Poisson-Poisson Cluster Field of Interferers**• Cluster centers distributed as spatial Poisson process over • Interferers distributed as spatial Poisson process Return Wireless Networking and Communications Group**Poisson-Poisson Cluster Field of Interferers**• Log-Characteristic function Return Wireless Networking and Communications Group**Poisson-Poisson Cluster Field of Interferers**Return • Simulation Results (tail probability) Case III: Infinite-area with guard zone Case I: Entire Plane Gaussian and Gaussian mixture models are not applicable since mean intensity is infinite Wireless Networking and Communications Group**Poisson-Poisson Cluster Field of Interferers**• Simulation Results (tail probability) Return Case II: Finite area annular region Wireless Networking and Communications Group**Gaussian Mixture vs. Alpha Stable**• Gaussian Mixture vs. Symmetric Alpha Stable Return Wireless Networking and Communications Group**[Middleton, 1999]**Middleton Class A, B and C Models • Class A Narrowband interference (“coherent” reception)Uniquely represented by 2 parameters • Class B Broadband interference (“incoherent” reception)Uniquely represented by six parameters • Class C Sum of Class A and Class B (approx. Class B) Return Wireless Networking and Communications Group**Parameter**Description Range Overlap Index. Product of average number of emissions per second and mean duration of typical emission A [10-2, 1] Gaussian Factor. Ratio of second-order moment of Gaussian component to that of non-Gaussian component Γ [10-6, 1] Middleton Class A model • Probability Density Function Return PDF for A = 0.15,= 0.8 Wireless Networking and Communications Group